Fuzzy Coordination of FACTS Controllers for Power systems
ELECTRICAL & ELECTRONICS ENGINEERING
K.Sravani Srinija (3/4 EEE) G.Annapurneswari (3/4 EEE) Email: [email protected] Email: [email protected]
Ph: 9963160279 Ph: 9290196174
Narasaroapet Engineering College
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ABSTRACT
This paper concerns the optimization
and coordination of the conventional FACTS
(Flexible AC Transmission Systems) damping
controllers in multimachine power system.
Firstly, the parameters of FACTS controller are
optimized. Then, a hybrid fuzzy logic
controller for the coordination of FACTS
controllers is presented. This coordination
method is well suitable to series connected
FACTS devices like UPFC, TCSC etc. in
damping multi-modal oscillations in multi-
machine power systems. Digital simulations of
a multi-machine power system subjected to a
wide variety of disturbances and different
structures validate the efficiency of the new
approach.
Keywords:
FACTS,
Fuzzy Logic,
Coordination,
Fuzzy- Coordination Controller,
Damping,
Stability
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1. INTRODUCTION
Nowadays, FACTS devices can be used
to control the power flow and enhance system
stability. They are playing an increasing and
major role in the operation and control of
power systems. The UPFC (Unified Power
Flow Controller) is the most versatile and
powerful FACTS device .The parameters in the
transmission line, i.e. line impedance, terminal
voltages, and voltage angle can be controlled
by UPFC. It is used for independent control of
real and reactive power in transmission lines.
Moreover, the UPFC can be used for voltage
support and damping of electromechanical
oscillations. In this paper, a multimachine
system with UPFC is simulated.
Damping of electromechanical
oscillations between interconnected
synchronous generators is necessary for secure
system operation. A well-designed FACTS
controller can not only increase the
transmission capability but also improve the
power system stability. A series of approaches
have been made in developing damping control
strategy for FACTS devices. The researches
are mostly based on single machine system.
However, FACTS devices are always installed
in multi-machine systems. The coordination
between FACTS controllers and other power
system controllers is very important.
Fuzzy-coordination controller is
presented in this paper for the coordinated of
traditional FACTS controllers. The fuzzy logic
controllers are rule-based controllers in which
a set of rules represents a control decision
mechanism to adjust the effect of certain cases
coming from power system. Furthermore,
fuzzy logic controllers do not require a
mathematical model of the system. They can
cover a wider range of operating conditions
and they are robust.
This paper focuses on the optimization
of conventional power oscillation damping
(POD) controllers and fuzzy logic coordination
of them. By using fuzzy-coordination
controller, the coordination objectives of the
FACTS devices are quite well achieved.
2. SYSTEM MODEL
2.1. Power System Model
A three machine nine bus interconnected
power system is simulated in this paper. There
are two UPFCs in the power system: between
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Bus2 Bus3 and, Bus6 Bus7. The diagram of the
power system model is shown in Fig. 1.
2.2. UPFC Model (UPFC Theory)
Basically, the UPFC have two
voltage source inverters (VSI) sharing a
common dc storage capacitor. It is connected
to the system through two coupling
transformers. One VSI is connected in shunt to
the system via a shunt transformer. The other
one is connected in series through a series
transformer. The UPFC scheme is shown in
Fig. 2.
The UPFC has several operating modes. Two
control modes are possible for the shunt
control:
1) VAR control mode: the reference input is
an inductive or capacitive Var request;
2) Automatic voltage control mode: the goal
is to maintain the transmission line voltage at
the connection point to a reference value.
By the control of series voltage, UPFC can be
operated in four different ways
1) Direct voltage injection mode: the reference
inputs are directly the magnitude and phase
angle of the series voltage;
2) Phase angle shifter emulation mode: the
reference input is phase displacement between
the sending end voltage and the receiving end
voltage;
3) Line impedance emulation mode: the
reference input is an impedance value to insert
in series with the line impedance;
4) Automatic power flow control mode: the
reference inputs are values of P and Q to
maintain on the transmission line despite
system changes.
Generally, for damping of power system
oscillations, UPFC will be operated in the direct
voltage injection mode. The mathematic model
of UPFC for the dynamic simulation is shown in
Fig.3
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3. CONTROL SCHEME
3.1. Traditional FACTS Damping Control
Scheme
Under a large disturbance, line
impedance emulation mode will be used to
improve first swing stability. For damping of
the subsequent swings, as suggested before,
UPFC will be operated in the direct voltage
injection mode. In this mode, the UPFC output
is the series compensation voltage V se. This
voltage is perpendicular to the line current I
line and the phase angle of I line is ahead of V
se. Thus, as shown in Fig.4, the damping
control of the UPFC is the same as a TCSC
POD control scheme. By the control of the
magnitude of V se, the series compensation
damping control can be achieved.
3.2. POD Controller
Commonly the POD controllers involve
a transfer function consisting of an
amplification link, a washout link and two
lead-lag links. A block diagram of the
conventional POD controller is illustrated in
Fig. 5. In this paper the active power of the
transmission line is used as input signal
The UPFC POD controller works
effectively in single machine system. In order
to improve the dynamic performance of a
multi-machine system, the behavior of the
controllers must be coordinated. Otherwise the
power system will be deteriorated.
3.3. Fuzzy Logic Control
In order to keep the advantage of the
existing POD controller and to improve its
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control performance in multimachine systems,
the hybrid fuzzy coordinated controller is
suggested in this paper.
Fuzzy logic controller is one of the
most practically successful approaches for
utilizing the qualitative knowledge of a system
to design a controller .In this paper the main
function of the fuzzy logic control is to
coordinate the operation of FACTS controllers.
In section 4 the design of the fuzzy logic
coordinated controller is presented in detail.
4. PARAMETER OPTIMIZATION AND
CONTROLLER DESIGN
4.1. Parameter Optimization for a Single
Machine POD Controller
In order to work effectively under
different operating conditions, many researches
are made on the controller parameter
optimization. Parameters of the POD controller
can be adjusted either by trial and error or by
optimization technique. In this paper the
parameters of the POD controller are optimized
using a nonlinear programming algorithm.
Originally, the aim of the parameter
optimization is to damp oscillations of power
systems where the UPFCs are installed. This
objective can be formulated as the
minimization of a nonlinear programming
problem expressed as follows:
where f(x) is the objective function, x are the
parameters of the POD controller. A(x) are the
equality functions and B(x) are the inequality
functions respectively. Particularly B(x)
indicate the restrictions of the POD parameter.
(i.e. the restrictions of lead-lag links and wash-
out links). In this simulation, only the
inequality functions B(x) are necessary.
The objective function is extremely important
for the parameter optimization. In this paper
the objective function is defined as follows:
where, δ(t, x) is the power angle curve of the
generator and t1 is the time range of the
simulation. With the variation of the controller
parameters x, the δ(t, x) will also be changed.
The power system simulation program PSD
(Power System Dynamic) is employed in this
simulation to evaluate the performance of the
POD controller.
Equation (1) is a general parameter-
constrained nonlinear optimization problem
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and can be solved successfully. In this paper
the Matlab Optimization Toolbox is applied.
The optimization starts with the pre-
selected initial values of the POD controller.
Then the nonlinear algorithm is used to
iteratively adjust the parameters, until the
objective function (2) is minimized. These so
determined parameters are the optimal settings
of the POD controller.
The flow chart of the parameter
optimization is shown in Fig. 6 the proposed
optimization algorithm was realized in a single
machine power system. In this optimization the
prefault state and post-fault state are the same
where δ(0)= δ(∞) . The optimized parameters
are given in Appendix 2.
4.2. Fuzzy Logic Coordinated Controller
Design
Most of the FACTS POD controllers
belong to the PI (proportional integral) type
and work effectively in single machine system.
Especially, after the parameter optimization,
the damping of power system oscillations is
perfectly achieved. However the performance
of the above mentioned POD controllers
deteriorates in multi-machine system.
Therefore the coordination between POD
controllers must be taken into account.
To cope with the coordination
problem, the optimization based coordination
and the feedback signal based coordination
have been developed. Also fuzzy logic has
successfully been applied to coordination. The
method used in is using the fuzzy logic
controller to coordinate the input signal of the
FACTS controller.
In this paper the fuzzy logic
controller is to coordinate the parameters of
FACTS controllers. The structure of the
proposed fuzzy-coordination controller is
shown in Fig. 7. Where the inputs P UPFC1
and P UPFC2 are the active power flow
through the UPFC1 and UPFC2. The output
signals are command signals adjusted to the
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UPFC controllers 1 and 2. In this way, the
conventional POD controllers are tuned by
using fuzzy-coordination controllers. The fuzzy
coordination controller involves Fuzzification,
Inference and Defuzzification unit.
4.2.1 Fuzzification
Fuzzification is a process whereby
the input variables are mapped onto fuzzy
variables (linguistic variables). Each fuzzified
variable has a certain membership function.
The inputs are fuzzified using three fuzzy sets:
B (big), M (medium) and S (small), as shown
in Fig. 8.
The membership function of the small set is:
Where x, namely P UPFC1 or P UPFC2, is the
input to the fuzzy controller. Similarly the big
set membership function is:
and the medium set membership function is:
The parameters L and K, as shown in Appendix
3, are determined basing upon the rated values
of UPFCs. These parameters can also be
optimized by using the simulation results.
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4.2.2 Inference
Control decisions are made based on
the fuzzified variables. Inference involves rules
for determining output decisions. Due to the
input variables having three fuzzified variables,
the fuzzy-coordination controller has nine rules
for each UPFC controller. The rules can be
obtained from the system operation and the
knowledge of the operator. Table 1 shows the
inference system.
To determine the degree of
memberships for output variables, the Min-
Max inference is applied. Both of the two
UPFC controllers use the same inference
system. Only the inputs of them are exchanged.
(as shown in Fig. 7)
4.2.3 Defuzzification
The output variables of the inference
system are linguistic variables. They must be
converted to numerical output. The fuzzy-
coordination controller uses centroid method.
The output of the fuzzy-coordination controller
is
where i u corresponds to the value of control
output for which the membership values in the
output sets are equal to unity.
5. SIMULATION RESULTS
5.1. Parameter optimization
The parameter optimization is made in
single machine system. Fig. 9 demonstrates the
improvement in damping of power system
oscillation. The initial and optimized values of
the POD controller are given Appendix 2. Fig.
9. Parameter optimization in a single machine
infinite bus system.
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5.2. Simulation in multi-machine system
Using the multi-machine power system
shown in Fig. 1, different disturbances and
different network parameters are simulated.
The performance of the fuzzy-coordination
controller for UPFC in damping power system
oscillations is examined. The following
simulations are made for evaluating the
performance of the proposed controller. In this
paper machine G3 is taken as the reference.
Case 1: Three-phase fault at Bus 2
A three-phase fault of 100 ms duration
is simulated at Bus 2. Fig. 10 presents the
results of the examined power system with
fuzzy-coordination controller. From Fig. 10 it
can be seen that with the proposed controller,
the dynamic performance of the power system
is quite improved. The pre-fault operating
condition (in p.u.) is: P1=0.105, P2=0.185.
Case 2: Changing of operation conditions
(Three-phase fault at Bus 3)
To validate the robustness of fuzzy-
coordination controller the pre-fault operating
conditions of the power network is changed to
P1=0.195, P2=0.28. Moreover the fault type is
also different: a three-phase fault of 110 ms
duration is simulated at Bus 3. Fig. 11 shows
the results of the simulation. The proposed
controller acts pretty well with the variation of
operation condition.
Case 3: Changing of network parameters
(Three-phase fault at Bus 9)
In order to verify the performance of
the fuzzy coordination controller for the
changing of system parameters, the reactance
of transformers T1 and T2 are increased by
20%. A three-phase fault of 100 ms is
simulated at Bus 9.
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The simulation results, as shown
in Fig. 12, illustrate that the proposed
controller is robust in parametric change. The
pre-fault operating condition (in p.u.) is:
P1=0.10, P2=0.120.
6. CONCLUSIONS
The paper presents a new fuzzy-coordination controller for the FACTS devices in a multi-machine power system to damp the electromechanical oscillations. The fuzzy coordination controller is designed based on the conventional POD controllers. The amplification part of the conventional controller is modified by the fuzzy coordination controller. The performance of the proposed method is simulated over a wide range of operating conditions and disturbances and its robustness is proved. Both inter-area and local modes oscillations are quite damped using this new controller. The proposed control scheme adopts the advantages of the conventional POD controller and it is not only robust but also simple and being easy to be realized in power system.
REFERENCES :
HVDC Transmission and distribution systems by Gupta,
V. Sitnikov, W. Breuer, D. Povh, D. Retzmann, E. Teltsch, benefits of Powe r electronics for
Transmission Enhancement・ Load-Flow Analysis with Respect to a
possible synchronous Interconnection of Networks of
UCTE and IPS/UPS.
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